Overview

Brought to you by YData

Dataset statistics

Number of variables33
Number of observations1673
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory431.4 KiB
Average record size in memory264.1 B

Variable types

Numeric17
Categorical11
Text5

Alerts

Plancha has constant value " " Constant
Origen has constant value "Central" Constant
Prof_Húme has constant value " " Constant
sigsonia_1 has constant value " " Constant
CE is highly overall correlated with Cond_Seco and 5 other fieldsHigh correlation
Cond_Seco is highly overall correlated with CE and 5 other fieldsHigh correlation
Cond_seco1 is highly overall correlated with T_seco1 and 2 other fieldsHigh correlation
F30 is highly overall correlated with ID_TOTAL and 1 other fieldsHigh correlation
ID_TOTAL is highly overall correlated with F30 and 1 other fieldsHigh correlation
Método_de is highly overall correlated with Tipo_de_NiHigh correlation
OBJECTID is highly overall correlated with F30 and 1 other fieldsHigh correlation
Observacio is highly overall correlated with sigsonia__High correlation
SAL is highly overall correlated with CE and 5 other fieldsHigh correlation
STD_seco is highly overall correlated with CE and 5 other fieldsHigh correlation
Sal_seco is highly overall correlated with CE and 5 other fieldsHigh correlation
T_seco1 is highly overall correlated with Cond_seco1 and 2 other fieldsHigh correlation
Tipo_de_Ni is highly overall correlated with Método_deHigh correlation
X is highly overall correlated with CE and 5 other fieldsHigh correlation
Y is highly overall correlated with CE and 5 other fieldsHigh correlation
pH_Humedo is highly overall correlated with Cond_seco1 and 2 other fieldsHigh correlation
sigsonia__ is highly overall correlated with Cond_seco1 and 3 other fieldsHigh correlation
Condición is highly imbalanced (70.9%) Imbalance
Observacio is highly imbalanced (90.7%) Imbalance
OBJECTID is uniformly distributed Uniform
OBJECTID has unique values Unique
ID_TOTAL has unique values Unique
pH_seco has 138 (8.2%) zeros Zeros
Cond_Seco has 135 (8.1%) zeros Zeros
T_Seco has 161 (9.6%) zeros Zeros
STD_seco has 135 (8.1%) zeros Zeros
Sal_seco has 135 (8.1%) zeros Zeros
sigsonia__ has 1538 (91.9%) zeros Zeros
pH_Humedo has 1505 (90.0%) zeros Zeros
Cond_seco1 has 1505 (90.0%) zeros Zeros
T_seco1 has 1505 (90.0%) zeros Zeros

Reproduction

Analysis started2026-02-24 01:55:39.740831
Analysis finished2026-02-24 01:56:08.215880
Duration28.48 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

OBJECTID
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct1673
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean841.75912
Minimum1
Maximum1683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:08.318669image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile88.6
Q1423
median841
Q31259
95-th percentile1599.4
Maximum1683
Range1682
Interquartile range (IQR)836

Descriptive statistics

Standard deviation484.42152
Coefficient of variation (CV)0.57548711
Kurtosis-1.1944351
Mean841.75912
Median Absolute Deviation (MAD)418
Skewness0.005362147
Sum1408263
Variance234664.21
MonotonicityStrictly increasing
2026-02-23T20:56:08.568245image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1683 1
 
0.1%
1 1
 
0.1%
2 1
 
0.1%
3 1
 
0.1%
4 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
1667 1
 
0.1%
Other values (1663) 1663
99.4%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
11 1
0.1%
ValueCountFrequency (%)
1683 1
0.1%
1682 1
0.1%
1681 1
0.1%
1680 1
0.1%
1679 1
0.1%
1678 1
0.1%
1677 1
0.1%
1676 1
0.1%
1675 1
0.1%
1674 1
0.1%

ID_TOTAL
Real number (ℝ)

High correlation  Unique 

Distinct1673
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean918.8147
Minimum1
Maximum1809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:08.681259image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile95.2
Q1490
median917
Q31353
95-th percentile1717.4
Maximum1809
Range1808
Interquartile range (IQR)863

Descriptive statistics

Standard deviation508.2901
Coefficient of variation (CV)0.55320196
Kurtosis-1.1390615
Mean918.8147
Median Absolute Deviation (MAD)431
Skewness-0.01259797
Sum1537177
Variance258358.82
MonotonicityStrictly increasing
2026-02-23T20:56:08.794477image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1809 1
 
0.1%
1 1
 
0.1%
2 1
 
0.1%
3 1
 
0.1%
4 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
1791 1
 
0.1%
Other values (1663) 1663
99.4%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
11 1
0.1%
ValueCountFrequency (%)
1809 1
0.1%
1808 1
0.1%
1807 1
0.1%
1806 1
0.1%
1805 1
0.1%
1804 1
0.1%
1803 1
0.1%
1802 1
0.1%
1801 1
0.1%
1800 1
0.1%

ID_PROYECT
Real number (ℝ)

Distinct150
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.790197
Minimum1
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:08.901574image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q119
median40
Q369
95-th percentile114.4
Maximum150
Range149
Interquartile range (IQR)50

Descriptive statistics

Standard deviation34.123907
Coefficient of variation (CV)0.72929606
Kurtosis-0.20416857
Mean46.790197
Median Absolute Deviation (MAD)24
Skewness0.73482806
Sum78280
Variance1164.441
MonotonicityNot monotonic
2026-02-23T20:56:09.018188image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 28
 
1.7%
3 26
 
1.6%
4 26
 
1.6%
1 25
 
1.5%
5 24
 
1.4%
7 24
 
1.4%
11 24
 
1.4%
8 24
 
1.4%
6 23
 
1.4%
9 22
 
1.3%
Other values (140) 1427
85.3%
ValueCountFrequency (%)
1 25
1.5%
2 28
1.7%
3 26
1.6%
4 26
1.6%
5 24
1.4%
6 23
1.4%
7 24
1.4%
8 24
1.4%
9 22
1.3%
10 21
1.3%
ValueCountFrequency (%)
150 1
0.1%
149 1
0.1%
148 1
0.1%
147 1
0.1%
146 1
0.1%
145 1
0.1%
144 1
0.1%
143 1
0.1%
142 1
0.1%
141 1
0.1%

Plancha
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
1673 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1673
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
1673
100.0%

Length

2026-02-23T20:56:09.116223image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-23T20:56:09.164378image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1673
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1673
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1673
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1673
100.0%

No_Consecu
Real number (ℝ)

Distinct55
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.588763
Minimum1
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:09.238980image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median11
Q322
95-th percentile38
Maximum55
Range54
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.605535
Coefficient of variation (CV)0.79551193
Kurtosis0.14537044
Mean14.588763
Median Absolute Deviation (MAD)7
Skewness0.92596851
Sum24407
Variance134.68844
MonotonicityNot monotonic
2026-02-23T20:56:09.349676image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 102
 
6.1%
2 101
 
6.0%
3 89
 
5.3%
4 85
 
5.1%
5 76
 
4.5%
6 73
 
4.4%
7 68
 
4.1%
8 66
 
3.9%
9 65
 
3.9%
10 58
 
3.5%
Other values (45) 890
53.2%
ValueCountFrequency (%)
1 102
6.1%
2 101
6.0%
3 89
5.3%
4 85
5.1%
5 76
4.5%
6 73
4.4%
7 68
4.1%
8 66
3.9%
9 65
3.9%
10 58
3.5%
ValueCountFrequency (%)
55 1
 
0.1%
54 1
 
0.1%
53 1
 
0.1%
52 1
 
0.1%
51 1
 
0.1%
50 2
 
0.1%
49 2
 
0.1%
48 5
0.3%
47 5
0.3%
46 4
0.2%

Tipo_de_Ca
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
Pozo
813 
Aljibe
813 
Manantial
 
47

Length

Max length9
Median length6
Mean length5.112373
Min length4

Characters and Unicode

Total characters8553
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPozo
2nd rowAljibe
3rd rowAljibe
4th rowAljibe
5th rowPozo

Common Values

ValueCountFrequency (%)
Pozo 813
48.6%
Aljibe 813
48.6%
Manantial 47
 
2.8%

Length

2026-02-23T20:56:09.443556image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-23T20:56:09.493154image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
ValueCountFrequency (%)
pozo 813
48.6%
aljibe 813
48.6%
manantial 47
 
2.8%

Most occurring characters

ValueCountFrequency (%)
o 1626
19.0%
l 860
10.1%
i 860
10.1%
z 813
9.5%
P 813
9.5%
A 813
9.5%
j 813
9.5%
b 813
9.5%
e 813
9.5%
a 141
 
1.6%
Other values (3) 188
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8553
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1626
19.0%
l 860
10.1%
i 860
10.1%
z 813
9.5%
P 813
9.5%
A 813
9.5%
j 813
9.5%
b 813
9.5%
e 813
9.5%
a 141
 
1.6%
Other values (3) 188
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8553
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1626
19.0%
l 860
10.1%
i 860
10.1%
z 813
9.5%
P 813
9.5%
A 813
9.5%
j 813
9.5%
b 813
9.5%
e 813
9.5%
a 141
 
1.6%
Other values (3) 188
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8553
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1626
19.0%
l 860
10.1%
i 860
10.1%
z 813
9.5%
P 813
9.5%
A 813
9.5%
j 813
9.5%
b 813
9.5%
e 813
9.5%
a 141
 
1.6%
Other values (3) 188
 
2.2%

Sitio
Text

Distinct1219
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:09.784328image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Length

Max length46
Median length40
Mean length10.043036
Min length1

Characters and Unicode

Total characters16802
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1111 ?
Unique (%)66.4%

Sample

1st rowSiapana
2nd rowIruapaa
3rd rowKashinas
4th rowPolujalii
5th rowSiapana
ValueCountFrequency (%)
finca 215
 
8.1%
154
 
5.8%
la 129
 
4.9%
el 64
 
2.4%
comunidad 38
 
1.4%
pozo 31
 
1.2%
de 31
 
1.2%
villa 29
 
1.1%
los 28
 
1.1%
las 23
 
0.9%
Other values (1249) 1899
71.9%
2026-02-23T20:56:10.173483image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2820
16.8%
1463
 
8.7%
i 1183
 
7.0%
o 1025
 
6.1%
n 1009
 
6.0%
r 917
 
5.5%
e 733
 
4.4%
l 633
 
3.8%
c 606
 
3.6%
u 604
 
3.6%
Other values (70) 5809
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16802
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2820
16.8%
1463
 
8.7%
i 1183
 
7.0%
o 1025
 
6.1%
n 1009
 
6.0%
r 917
 
5.5%
e 733
 
4.4%
l 633
 
3.8%
c 606
 
3.6%
u 604
 
3.6%
Other values (70) 5809
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16802
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2820
16.8%
1463
 
8.7%
i 1183
 
7.0%
o 1025
 
6.1%
n 1009
 
6.0%
r 917
 
5.5%
e 733
 
4.4%
l 633
 
3.8%
c 606
 
3.6%
u 604
 
3.6%
Other values (70) 5809
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16802
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2820
16.8%
1463
 
8.7%
i 1183
 
7.0%
o 1025
 
6.1%
n 1009
 
6.0%
r 917
 
5.5%
e 733
 
4.4%
l 633
 
3.8%
c 606
 
3.6%
u 604
 
3.6%
Other values (70) 5809
34.6%

Origen
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
Central
1673 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters11711
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCentral
2nd rowCentral
3rd rowCentral
4th rowCentral
5th rowCentral

Common Values

ValueCountFrequency (%)
Central 1673
100.0%

Length

2026-02-23T20:56:10.252837image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-23T20:56:10.308905image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
ValueCountFrequency (%)
central 1673
100.0%

Most occurring characters

ValueCountFrequency (%)
C 1673
14.3%
e 1673
14.3%
n 1673
14.3%
t 1673
14.3%
r 1673
14.3%
a 1673
14.3%
l 1673
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11711
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 1673
14.3%
e 1673
14.3%
n 1673
14.3%
t 1673
14.3%
r 1673
14.3%
a 1673
14.3%
l 1673
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11711
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 1673
14.3%
e 1673
14.3%
n 1673
14.3%
t 1673
14.3%
r 1673
14.3%
a 1673
14.3%
l 1673
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11711
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 1673
14.3%
e 1673
14.3%
n 1673
14.3%
t 1673
14.3%
r 1673
14.3%
a 1673
14.3%
l 1673
14.3%

X
Real number (ℝ)

High correlation 

Distinct1668
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1169876
Minimum1055701
Maximum1320375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:10.374916image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum1055701
5-th percentile1109764.8
Q11134128
median1160481.4
Q31191299
95-th percentile1294381.5
Maximum1320375
Range264674
Interquartile range (IQR)57171

Descriptive statistics

Standard deviation51232.999
Coefficient of variation (CV)0.043793529
Kurtosis0.87372975
Mean1169876
Median Absolute Deviation (MAD)27998.38
Skewness1.1147353
Sum1.9572026 × 109
Variance2.6248202 × 109
MonotonicityNot monotonic
2026-02-23T20:56:10.494261image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1177374 3
 
0.2%
1197457 2
 
0.1%
1294386.809 2
 
0.1%
1124841 2
 
0.1%
1299085.737 1
 
0.1%
1298371.675 1
 
0.1%
1298003.653 1
 
0.1%
1299536.07 1
 
0.1%
1218411 1
 
0.1%
1225063 1
 
0.1%
Other values (1658) 1658
99.1%
ValueCountFrequency (%)
1055701 1
0.1%
1058434 1
0.1%
1059661 1
0.1%
1059896 1
0.1%
1065824 1
0.1%
1069055 1
0.1%
1073541 1
0.1%
1074564 1
0.1%
1074799 1
0.1%
1077675 1
0.1%
ValueCountFrequency (%)
1320375 1
0.1%
1318975 1
0.1%
1318190 1
0.1%
1315962 1
0.1%
1315939 1
0.1%
1314192 1
0.1%
1312876 1
0.1%
1312308 1
0.1%
1310445.702 1
0.1%
1309622 1
0.1%

Y
Real number (ℝ)

High correlation 

Distinct1666
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1751737.4
Minimum1646007
Maximum1870665
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:10.594354image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum1646007
5-th percentile1667749.6
Q11729621.4
median1755844
Q31778759.2
95-th percentile1843099.8
Maximum1870665
Range224658
Interquartile range (IQR)49137.714

Descriptive statistics

Standard deviation47889.209
Coefficient of variation (CV)0.02733812
Kurtosis-0.25663857
Mean1751737.4
Median Absolute Deviation (MAD)25008
Skewness0.0085494095
Sum2.9306567 × 109
Variance2.2933763 × 109
MonotonicityNot monotonic
2026-02-23T20:56:10.704414image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1779724 2
 
0.1%
1834680.992 2
 
0.1%
1788127 2
 
0.1%
1758313 2
 
0.1%
1787531.686 2
 
0.1%
1736801 2
 
0.1%
1844852 2
 
0.1%
1765831 1
 
0.1%
1720222 1
 
0.1%
1769174 1
 
0.1%
Other values (1656) 1656
99.0%
ValueCountFrequency (%)
1646007 1
0.1%
1646601 1
0.1%
1646648 1
0.1%
1647129 1
0.1%
1647612 1
0.1%
1647823 1
0.1%
1649442 1
0.1%
1650340 1
0.1%
1651483 1
0.1%
1651611 1
0.1%
ValueCountFrequency (%)
1870665 1
0.1%
1869611 1
0.1%
1868440 1
0.1%
1868357 1
0.1%
1868225 1
0.1%
1865508 1
0.1%
1861367 1
0.1%
1860211 1
0.1%
1860028 1
0.1%
1858655 1
0.1%

Uso_del_Su
Categorical

Distinct43
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
Forestal
641 
285 
Ganaderia
219 
Ganadería
171 
Forestal - Ganaderia
70 
Other values (38)
287 

Length

Max length34
Median length33
Mean length8.427376
Min length1

Characters and Unicode

Total characters14099
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)1.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
Forestal 641
38.3%
285
17.0%
Ganaderia 219
 
13.1%
Ganadería 171
 
10.2%
Forestal - Ganaderia 70
 
4.2%
Agricultura 60
 
3.6%
Urbano 41
 
2.5%
Rancheria 38
 
2.3%
ganaderia 18
 
1.1%
Gananderia y Rancheria 13
 
0.8%
Other values (33) 117
 
7.0%

Length

2026-02-23T20:56:10.816986image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
forestal 731
43.0%
ganaderia 380
22.3%
ganadería 171
 
10.1%
agricultura 98
 
5.8%
94
 
5.5%
rancheria 77
 
4.5%
y 56
 
3.3%
urbano 43
 
2.5%
gananderia 13
 
0.8%
industrial 9
 
0.5%
Other values (12) 29
 
1.7%

Most occurring characters

ValueCountFrequency (%)
a 2781
19.7%
r 1653
11.7%
e 1401
9.9%
t 851
 
6.0%
l 850
 
6.0%
o 795
 
5.6%
s 749
 
5.3%
F 737
 
5.2%
n 724
 
5.1%
i 591
 
4.2%
Other values (20) 2967
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14099
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2781
19.7%
r 1653
11.7%
e 1401
9.9%
t 851
 
6.0%
l 850
 
6.0%
o 795
 
5.6%
s 749
 
5.3%
F 737
 
5.2%
n 724
 
5.1%
i 591
 
4.2%
Other values (20) 2967
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14099
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2781
19.7%
r 1653
11.7%
e 1401
9.9%
t 851
 
6.0%
l 850
 
6.0%
o 795
 
5.6%
s 749
 
5.3%
F 737
 
5.2%
n 724
 
5.1%
i 591
 
4.2%
Other values (20) 2967
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14099
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2781
19.7%
r 1653
11.7%
e 1401
9.9%
t 851
 
6.0%
l 850
 
6.0%
o 795
 
5.6%
s 749
 
5.3%
F 737
 
5.2%
n 724
 
5.1%
i 591
 
4.2%
Other values (20) 2967
21.0%

Condición
Categorical

Imbalance 

Distinct13
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
Productivo
1335 
180 
Reserva
 
93
Abandonado
 
50
En construcción
 
4
Other values (8)
 
11

Length

Max length25
Median length10
Mean length8.881052
Min length1

Characters and Unicode

Total characters14858
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.4%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
Productivo 1335
79.8%
180
 
10.8%
Reserva 93
 
5.6%
Abandonado 50
 
3.0%
En construcción 4
 
0.2%
reserva 3
 
0.2%
abandonado 2
 
0.1%
productivo 1
 
0.1%
En adecuación 1
 
0.1%
Nuevo 1
 
0.1%
Other values (3) 3
 
0.2%

Length

2026-02-23T20:56:11.054437image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
productivo 1336
89.0%
reserva 98
 
6.5%
abandonado 53
 
3.5%
en 5
 
0.3%
construcción 4
 
0.3%
adecuación 1
 
0.1%
nuevo 1
 
0.1%
seco 1
 
0.1%
recien 1
 
0.1%
construido 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 2786
18.8%
d 1444
9.7%
r 1442
9.7%
v 1435
9.7%
c 1352
9.1%
i 1343
9.0%
u 1343
9.0%
t 1341
9.0%
P 1335
9.0%
a 208
 
1.4%
Other values (15) 829
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 2786
18.8%
d 1444
9.7%
r 1442
9.7%
v 1435
9.7%
c 1352
9.1%
i 1343
9.0%
u 1343
9.0%
t 1341
9.0%
P 1335
9.0%
a 208
 
1.4%
Other values (15) 829
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 2786
18.8%
d 1444
9.7%
r 1442
9.7%
v 1435
9.7%
c 1352
9.1%
i 1343
9.0%
u 1343
9.0%
t 1341
9.0%
P 1335
9.0%
a 208
 
1.4%
Other values (15) 829
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 2786
18.8%
d 1444
9.7%
r 1442
9.7%
v 1435
9.7%
c 1352
9.1%
i 1343
9.0%
u 1343
9.0%
t 1341
9.0%
P 1335
9.0%
a 208
 
1.4%
Other values (15) 829
 
5.6%
Distinct130
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:11.303828image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Length

Max length6
Median length1
Mean length1.3102212
Min length1

Characters and Unicode

Total characters2192
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)4.8%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
150 23
 
7.6%
70 15
 
4.9%
60 14
 
4.6%
100 10
 
3.3%
160 9
 
3.0%
30 9
 
3.0%
80 9
 
3.0%
120 8
 
2.6%
140 8
 
2.6%
36 8
 
2.6%
Other values (113) 191
62.8%
2026-02-23T20:56:11.643619image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1375
62.7%
0 207
 
9.4%
1 148
 
6.8%
5 78
 
3.6%
6 65
 
3.0%
3 64
 
2.9%
2 64
 
2.9%
7 44
 
2.0%
4 40
 
1.8%
8 39
 
1.8%
Other values (6) 68
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1375
62.7%
0 207
 
9.4%
1 148
 
6.8%
5 78
 
3.6%
6 65
 
3.0%
3 64
 
2.9%
2 64
 
2.9%
7 44
 
2.0%
4 40
 
1.8%
8 39
 
1.8%
Other values (6) 68
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1375
62.7%
0 207
 
9.4%
1 148
 
6.8%
5 78
 
3.6%
6 65
 
3.0%
3 64
 
2.9%
2 64
 
2.9%
7 44
 
2.0%
4 40
 
1.8%
8 39
 
1.8%
Other values (6) 68
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1375
62.7%
0 207
 
9.4%
1 148
 
6.8%
5 78
 
3.6%
6 65
 
3.0%
3 64
 
2.9%
2 64
 
2.9%
7 44
 
2.0%
4 40
 
1.8%
8 39
 
1.8%
Other values (6) 68
 
3.1%

Tipo_de_Ni
Categorical

High correlation 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
Estático
885 
505 
Estatico
166 
-
 
67
Dinámico
 
31
Other values (3)
 
19

Length

Max length8
Median length8
Mean length5.6066946
Min length1

Characters and Unicode

Total characters9380
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
Estático 885
52.9%
505
30.2%
Estatico 166
 
9.9%
- 67
 
4.0%
Dinámico 31
 
1.9%
estático 15
 
0.9%
Dinamico 2
 
0.1%
Estimado 2
 
0.1%

Length

2026-02-23T20:56:11.727009image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-23T20:56:11.794431image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
ValueCountFrequency (%)
estático 900
77.1%
estatico 166
 
14.2%
67
 
5.7%
dinámico 31
 
2.7%
dinamico 2
 
0.2%
estimado 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
t 2134
22.8%
i 1134
12.1%
o 1101
11.7%
c 1099
11.7%
s 1068
11.4%
E 1053
11.2%
á 931
9.9%
505
 
5.4%
a 170
 
1.8%
- 67
 
0.7%
Other values (5) 118
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9380
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 2134
22.8%
i 1134
12.1%
o 1101
11.7%
c 1099
11.7%
s 1068
11.4%
E 1053
11.2%
á 931
9.9%
505
 
5.4%
a 170
 
1.8%
- 67
 
0.7%
Other values (5) 118
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9380
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 2134
22.8%
i 1134
12.1%
o 1101
11.7%
c 1099
11.7%
s 1068
11.4%
E 1053
11.2%
á 931
9.9%
505
 
5.4%
a 170
 
1.8%
- 67
 
0.7%
Other values (5) 118
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9380
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 2134
22.8%
i 1134
12.1%
o 1101
11.7%
c 1099
11.7%
s 1068
11.4%
E 1053
11.2%
á 931
9.9%
505
 
5.4%
a 170
 
1.8%
- 67
 
0.7%
Other values (5) 118
 
1.3%

Método_de
Categorical

High correlation 

Distinct18
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
623 
Sonda Eléctrica
513 
Sonda eléctrica
190 
Sonda Electrica
160 
-
67 
Other values (13)
120 

Length

Max length19
Median length15
Mean length8.8977884
Min length1

Characters and Unicode

Total characters14886
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.3%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
623
37.2%
Sonda Eléctrica 513
30.7%
Sonda eléctrica 190
 
11.4%
Sonda Electrica 160
 
9.6%
- 67
 
4.0%
Estimado 49
 
2.9%
Sonda Eléctrico 24
 
1.4%
sonda 15
 
0.9%
Sonda Eelctrica 11
 
0.7%
Reportado 8
 
0.5%
Other values (8) 13
 
0.8%

Length

2026-02-23T20:56:11.885461image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sonda 920
46.9%
eléctrica 704
35.9%
electrica 163
 
8.3%
68
 
3.5%
estimado 50
 
2.5%
eléctrico 24
 
1.2%
eelctrica 11
 
0.6%
reportado 8
 
0.4%
cinta 4
 
0.2%
métrica 3
 
0.2%
Other values (5) 6
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a 1869
12.6%
c 1815
12.2%
1534
10.3%
o 1013
 
6.8%
d 979
 
6.6%
t 970
 
6.5%
i 964
 
6.5%
n 928
 
6.2%
r 918
 
6.2%
l 905
 
6.1%
Other values (15) 2991
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14886
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1869
12.6%
c 1815
12.2%
1534
10.3%
o 1013
 
6.8%
d 979
 
6.6%
t 970
 
6.5%
i 964
 
6.5%
n 928
 
6.2%
r 918
 
6.2%
l 905
 
6.1%
Other values (15) 2991
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14886
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1869
12.6%
c 1815
12.2%
1534
10.3%
o 1013
 
6.8%
d 979
 
6.6%
t 970
 
6.5%
i 964
 
6.5%
n 928
 
6.2%
r 918
 
6.2%
l 905
 
6.1%
Other values (15) 2991
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14886
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1869
12.6%
c 1815
12.2%
1534
10.3%
o 1013
 
6.8%
d 979
 
6.6%
t 970
 
6.5%
i 964
 
6.5%
n 928
 
6.2%
r 918
 
6.2%
l 905
 
6.1%
Other values (15) 2991
20.1%
Distinct789
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:12.210443image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Length

Max length10
Median length8
Mean length2.8750747
Min length1

Characters and Unicode

Total characters4810
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique631 ?
Unique (%)37.7%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
67
 
6.0%
3 16
 
1.4%
saltante 10
 
0.9%
4 8
 
0.7%
6 8
 
0.7%
5 7
 
0.6%
10 6
 
0.5%
9 6
 
0.5%
12 6
 
0.5%
18 5
 
0.4%
Other values (779) 980
87.6%
2026-02-23T20:56:12.619049image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 927
19.3%
1 602
12.5%
559
11.6%
2 457
9.5%
3 321
 
6.7%
4 318
 
6.6%
5 312
 
6.5%
6 264
 
5.5%
7 259
 
5.4%
8 254
 
5.3%
Other values (12) 537
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4810
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 927
19.3%
1 602
12.5%
559
11.6%
2 457
9.5%
3 321
 
6.7%
4 318
 
6.6%
5 312
 
6.5%
6 264
 
5.5%
7 259
 
5.4%
8 254
 
5.3%
Other values (12) 537
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4810
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 927
19.3%
1 602
12.5%
559
11.6%
2 457
9.5%
3 321
 
6.7%
4 318
 
6.6%
5 312
 
6.5%
6 264
 
5.5%
7 259
 
5.4%
8 254
 
5.3%
Other values (12) 537
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4810
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 927
19.3%
1 602
12.5%
559
11.6%
2 457
9.5%
3 321
 
6.7%
4 318
 
6.6%
5 312
 
6.5%
6 264
 
5.5%
7 259
 
5.4%
8 254
 
5.3%
Other values (12) 537
11.2%

Prof_Húme
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
1673 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1673
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
1673
100.0%

Length

2026-02-23T20:56:12.702066image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-23T20:56:12.736402image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1673
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1673
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1673
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1673
100.0%

Observacio
Categorical

High correlation  Imbalance 

Distinct23
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
1591 
No se puede medir
 
44
Se desconoce Pozorofundidad
 
8
No cuenta con tuberia Pozoara toma de niveles
 
6
No se puede medir niveles, ya que no tiene tuberia para introducir la sonda
 
3
Other values (18)
 
21

Length

Max length164
Median length1
Mean length2.5248057
Min length1

Characters and Unicode

Total characters4224
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.9%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
1591
95.1%
No se puede medir 44
 
2.6%
Se desconoce Pozorofundidad 8
 
0.5%
No cuenta con tuberia Pozoara toma de niveles 6
 
0.4%
No se puede medir niveles, ya que no tiene tuberia para introducir la sonda 3
 
0.2%
No se puede medir nivel, porque la platina no lo permite 2
 
0.1%
No se pudo medir 2
 
0.1%
No se puede medir nivel estático porque el pozo esta totalmente sellado 2
 
0.1%
No se midio estático porque porque el pozo tiene la boca sellada con una bomba manual, anteriormente se explotaba con molino de viento y ahora con bomba sumergible 1
 
0.1%
No se pudo medir nivel del agua 1
 
0.1%
Other values (13) 13
 
0.8%

Length

2026-02-23T20:56:12.844464image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 76
14.9%
se 72
 
14.1%
medir 60
 
11.8%
puede 51
 
10.0%
de 14
 
2.7%
nivel 13
 
2.5%
la 12
 
2.4%
tuberia 12
 
2.4%
niveles 10
 
2.0%
con 10
 
2.0%
Other values (77) 180
35.3%

Most occurring characters

ValueCountFrequency (%)
2020
47.8%
e 404
 
9.6%
o 230
 
5.4%
d 191
 
4.5%
i 147
 
3.5%
a 144
 
3.4%
r 130
 
3.1%
s 115
 
2.7%
u 111
 
2.6%
n 107
 
2.5%
Other values (33) 625
 
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2020
47.8%
e 404
 
9.6%
o 230
 
5.4%
d 191
 
4.5%
i 147
 
3.5%
a 144
 
3.4%
r 130
 
3.1%
s 115
 
2.7%
u 111
 
2.6%
n 107
 
2.5%
Other values (33) 625
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2020
47.8%
e 404
 
9.6%
o 230
 
5.4%
d 191
 
4.5%
i 147
 
3.5%
a 144
 
3.4%
r 130
 
3.1%
s 115
 
2.7%
u 111
 
2.6%
n 107
 
2.5%
Other values (33) 625
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2020
47.8%
e 404
 
9.6%
o 230
 
5.4%
d 191
 
4.5%
i 147
 
3.5%
a 144
 
3.4%
r 130
 
3.1%
s 115
 
2.7%
u 111
 
2.6%
n 107
 
2.5%
Other values (33) 625
 
14.8%

pH_seco
Real number (ℝ)

Zeros 

Distinct237
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7342977
Minimum0
Maximum9.56
Zeros138
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:12.954961image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.94
median7.26
Q37.61
95-th percentile8.124
Maximum9.56
Range9.56
Interquartile range (IQR)0.67

Descriptive statistics

Standard deviation2.0748129
Coefficient of variation (CV)0.30809641
Kurtosis6.2351945
Mean6.7342977
Median Absolute Deviation (MAD)0.34
Skewness-2.7581763
Sum11266.48
Variance4.3048487
MonotonicityNot monotonic
2026-02-23T20:56:13.086096image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 138
 
8.2%
7.26 25
 
1.5%
7.56 21
 
1.3%
6.95 19
 
1.1%
7.3 18
 
1.1%
7.24 17
 
1.0%
7.36 17
 
1.0%
7.13 17
 
1.0%
7.17 17
 
1.0%
7.35 16
 
1.0%
Other values (227) 1368
81.8%
ValueCountFrequency (%)
0 138
8.2%
4.19 1
 
0.1%
4.95 1
 
0.1%
5.35 1
 
0.1%
5.41 1
 
0.1%
5.53 1
 
0.1%
5.56 1
 
0.1%
5.61 1
 
0.1%
5.73 1
 
0.1%
5.88 1
 
0.1%
ValueCountFrequency (%)
9.56 1
 
0.1%
9.49 1
 
0.1%
9.47 2
0.1%
8.86 1
 
0.1%
8.74 1
 
0.1%
8.72 3
0.2%
8.59 1
 
0.1%
8.57 1
 
0.1%
8.53 1
 
0.1%
8.52 1
 
0.1%

Cond_Seco
Real number (ℝ)

High correlation  Zeros 

Distinct1387
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2914.2934
Minimum0
Maximum59830
Zeros135
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:13.192992image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1831.5
median1542
Q33223
95-th percentile9665
Maximum59830
Range59830
Interquartile range (IQR)2391.5

Descriptive statistics

Standard deviation4529.7646
Coefficient of variation (CV)1.5543269
Kurtosis43.323775
Mean2914.2934
Median Absolute Deviation (MAD)913.8
Skewness5.4194459
Sum4875612.8
Variance20518767
MonotonicityNot monotonic
2026-02-23T20:56:13.303520image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 135
 
8.1%
1006 5
 
0.3%
1101 4
 
0.2%
1137 3
 
0.2%
1148 3
 
0.2%
1240 3
 
0.2%
2546 3
 
0.2%
1316 3
 
0.2%
1218 3
 
0.2%
1409 3
 
0.2%
Other values (1377) 1508
90.1%
ValueCountFrequency (%)
0 135
8.1%
58.31 1
 
0.1%
68.43 1
 
0.1%
104.5 1
 
0.1%
131.4 1
 
0.1%
155.7 1
 
0.1%
172.5 1
 
0.1%
173.7 1
 
0.1%
177.4 1
 
0.1%
221.3 1
 
0.1%
ValueCountFrequency (%)
59830 1
0.1%
51260 1
0.1%
47700 1
0.1%
41490 1
0.1%
38850 1
0.1%
36830 1
0.1%
36050 1
0.1%
35440 1
0.1%
33330 1
0.1%
33030 1
0.1%

T_Seco
Real number (ℝ)

Zeros 

Distinct106
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.917215
Minimum0
Maximum41.4
Zeros161
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:13.412765image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q129.1
median30.9
Q332.3
95-th percentile33.7
Maximum41.4
Range41.4
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation9.3437037
Coefficient of variation (CV)0.33469327
Kurtosis4.7370061
Mean27.917215
Median Absolute Deviation (MAD)1.5
Skewness-2.5043396
Sum46705.5
Variance87.304799
MonotonicityNot monotonic
2026-02-23T20:56:13.530022image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 161
 
9.6%
25 60
 
3.6%
32.4 39
 
2.3%
30.7 39
 
2.3%
31.2 38
 
2.3%
31.1 37
 
2.2%
32.6 36
 
2.2%
30.3 36
 
2.2%
31.8 35
 
2.1%
32.3 35
 
2.1%
Other values (96) 1157
69.2%
ValueCountFrequency (%)
0 161
9.6%
20.2 2
 
0.1%
21.9 1
 
0.1%
22.8 1
 
0.1%
24.5 1
 
0.1%
25 60
 
3.6%
25.4 2
 
0.1%
25.5 1
 
0.1%
25.7 1
 
0.1%
26.3 1
 
0.1%
ValueCountFrequency (%)
41.4 1
 
0.1%
38.7 1
 
0.1%
37.6 2
0.1%
37 1
 
0.1%
36.8 1
 
0.1%
36.6 1
 
0.1%
36.1 2
0.1%
35.5 1
 
0.1%
35.4 3
0.2%
35.3 1
 
0.1%

STD_seco
Real number (ℝ)

High correlation  Zeros 

Distinct1437
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1431.7049
Minimum0
Maximum29320
Zeros135
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:13.634710image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1405.7
median751.4
Q31596
95-th percentile4941.1894
Maximum29320
Range29320
Interquartile range (IQR)1190.3

Descriptive statistics

Standard deviation2206.869
Coefficient of variation (CV)1.5414273
Kurtosis42.613753
Mean1431.7049
Median Absolute Deviation (MAD)448
Skewness5.3145219
Sum2395242.3
Variance4870270.9
MonotonicityNot monotonic
2026-02-23T20:56:13.752683image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 135
 
8.1%
1950 3
 
0.2%
578 3
 
0.2%
1284 3
 
0.2%
1044 3
 
0.2%
268.4 3
 
0.2%
774.9 2
 
0.1%
1259 2
 
0.1%
263.3 2
 
0.1%
1240 2
 
0.1%
Other values (1427) 1515
90.6%
ValueCountFrequency (%)
0 135
8.1%
1.016 1
 
0.1%
1.052 1
 
0.1%
1.255 1
 
0.1%
2.487 1
 
0.1%
7.54 1
 
0.1%
29.07 1
 
0.1%
33.37 1
 
0.1%
51.72 1
 
0.1%
64.87 1
 
0.1%
ValueCountFrequency (%)
29320 1
0.1%
25139.7744 1
0.1%
23370 1
0.1%
20330 1
0.1%
19030 1
0.1%
17670 1
0.1%
17370 1
0.1%
16330 1
0.1%
16190 1
0.1%
13760 1
0.1%

Sal_seco
Real number (ℝ)

High correlation  Zeros 

Distinct1194
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6735387
Minimum0
Maximum59.8
Zeros135
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:13.856321image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.457
median0.832
Q31.755
95-th percentile5.5564
Maximum59.8
Range59.8
Interquartile range (IQR)1.298

Descriptive statistics

Standard deviation3.0990628
Coefficient of variation (CV)1.8518024
Kurtosis106.95875
Mean1.6735387
Median Absolute Deviation (MAD)0.483
Skewness8.2095819
Sum2799.8302
Variance9.6041904
MonotonicityNot monotonic
2026-02-23T20:56:14.102375image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 135
 
8.1%
0.357 6
 
0.4%
0.603 5
 
0.3%
0.398 5
 
0.3%
0.702 5
 
0.3%
0.415 4
 
0.2%
0.452 4
 
0.2%
0.464 4
 
0.2%
0.666 4
 
0.2%
0.708 4
 
0.2%
Other values (1184) 1497
89.5%
ValueCountFrequency (%)
0 135
8.1%
0.085 1
 
0.1%
0.086 1
 
0.1%
0.089 1
 
0.1%
0.104 1
 
0.1%
0.117 1
 
0.1%
0.122 1
 
0.1%
0.135 1
 
0.1%
0.136 1
 
0.1%
0.137 1
 
0.1%
ValueCountFrequency (%)
59.8 1
0.1%
40.36 1
0.1%
31.13 1
0.1%
28.193 1
0.1%
26.76 1
0.1%
24.85 1
0.1%
23.47 1
0.1%
22.91 1
0.1%
22.51 1
0.1%
21 1
0.1%

sigsonia__
Real number (ℝ)

High correlation  Zeros 

Distinct132
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.188653
Minimum0
Maximum3445
Zeros1538
Zeros (%)91.9%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:14.212337image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10.44746
Maximum3445
Range3445
Interquartile range (IQR)0

Descriptive statistics

Standard deviation157.47089
Coefficient of variation (CV)12.919466
Kurtosis290.26517
Mean12.188653
Median Absolute Deviation (MAD)0
Skewness16.375018
Sum20391.617
Variance24797.081
MonotonicityNot monotonic
2026-02-23T20:56:14.332326image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1538
91.9%
5.099439 2
 
0.1%
6.127451 2
 
0.1%
7.446016 2
 
0.1%
49.309665 2
 
0.1%
17.190992 1
 
0.1%
10.752688 1
 
0.1%
10.058338 1
 
0.1%
9.242144 1
 
0.1%
9.596929 1
 
0.1%
Other values (122) 122
 
7.3%
ValueCountFrequency (%)
0 1538
91.9%
2.008839 1
 
0.1%
2.335357 1
 
0.1%
2.51004 1
 
0.1%
3.031222 1
 
0.1%
3.224766 1
 
0.1%
3.474635 1
 
0.1%
3.48675 1
 
0.1%
3.752345 1
 
0.1%
3.868472 1
 
0.1%
ValueCountFrequency (%)
3445 1
0.1%
2959 1
0.1%
2579 1
0.1%
2084 1
0.1%
1960 1
0.1%
1524 1
0.1%
1361 1
0.1%
952.6 1
0.1%
835.5 1
0.1%
640.8 1
0.1%

pH_Humedo
Real number (ℝ)

High correlation  Zeros 

Distinct95
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72083682
Minimum0
Maximum8.49
Zeros1505
Zeros (%)90.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:14.442646image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7.13
Maximum8.49
Range8.49
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1619913
Coefficient of variation (CV)2.9992798
Kurtosis5.2176616
Mean0.72083682
Median Absolute Deviation (MAD)0
Skewness2.6785712
Sum1205.96
Variance4.6742065
MonotonicityNot monotonic
2026-02-23T20:56:14.552285image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1505
90.0%
6.98 5
 
0.3%
6.99 5
 
0.3%
7.13 5
 
0.3%
7.12 4
 
0.2%
6.95 4
 
0.2%
7.09 4
 
0.2%
7.35 4
 
0.2%
7.29 3
 
0.2%
7.07 3
 
0.2%
Other values (85) 131
 
7.8%
ValueCountFrequency (%)
0 1505
90.0%
4.92 1
 
0.1%
5.98 1
 
0.1%
6.34 1
 
0.1%
6.44 1
 
0.1%
6.49 1
 
0.1%
6.56 2
 
0.1%
6.59 2
 
0.1%
6.62 2
 
0.1%
6.66 1
 
0.1%
ValueCountFrequency (%)
8.49 1
 
0.1%
8.02 1
 
0.1%
8 1
 
0.1%
7.99 1
 
0.1%
7.97 1
 
0.1%
7.91 1
 
0.1%
7.9 1
 
0.1%
7.88 1
 
0.1%
7.85 1
 
0.1%
7.78 3
0.2%

Cond_seco1
Real number (ℝ)

High correlation  Zeros 

Distinct163
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161.84088
Minimum0
Maximum10830
Zeros1505
Zeros (%)90.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:14.676165image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile972.48
Maximum10830
Range10830
Interquartile range (IQR)0

Descriptive statistics

Standard deviation777.76897
Coefficient of variation (CV)4.8057632
Kurtosis87.106216
Mean161.84088
Median Absolute Deviation (MAD)0
Skewness8.3861326
Sum270759.8
Variance604924.57
MonotonicityNot monotonic
2026-02-23T20:56:14.796292image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1505
90.0%
1620 2
 
0.1%
1145 2
 
0.1%
202.8 2
 
0.1%
1632 2
 
0.1%
1343 2
 
0.1%
1961 2
 
0.1%
917 1
 
0.1%
1394 1
 
0.1%
3100 1
 
0.1%
Other values (153) 153
 
9.1%
ValueCountFrequency (%)
0 1505
90.0%
114.1 1
 
0.1%
126.5 1
 
0.1%
180.8 1
 
0.1%
196 1
 
0.1%
202.8 2
 
0.1%
213.6 1
 
0.1%
220.4 1
 
0.1%
226.9 1
 
0.1%
257.6 1
 
0.1%
ValueCountFrequency (%)
10830 1
0.1%
10001 1
0.1%
9780 1
0.1%
9530 1
0.1%
8260 1
0.1%
8170 1
0.1%
7120 1
0.1%
6630 1
0.1%
5430 1
0.1%
5010 1
0.1%

T_seco1
Real number (ℝ)

High correlation  Zeros 

Distinct64
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9624626
Minimum0
Maximum34.4
Zeros1505
Zeros (%)90.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:14.904480image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile29.7
Maximum34.4
Range34.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.8953365
Coefficient of variation (CV)3.0026831
Kurtosis5.2836361
Mean2.9624626
Median Absolute Deviation (MAD)0
Skewness2.6881155
Sum4956.2
Variance79.127011
MonotonicityNot monotonic
2026-02-23T20:56:15.019475image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1505
90.0%
31.5 8
 
0.5%
30.6 7
 
0.4%
30.7 6
 
0.4%
28 6
 
0.4%
28.4 6
 
0.4%
30.2 6
 
0.4%
30.3 6
 
0.4%
28.6 5
 
0.3%
28.9 5
 
0.3%
Other values (54) 113
 
6.8%
ValueCountFrequency (%)
0 1505
90.0%
20.1 1
 
0.1%
20.2 1
 
0.1%
20.5 1
 
0.1%
23.1 1
 
0.1%
24.4 1
 
0.1%
25.7 1
 
0.1%
26 1
 
0.1%
26.4 3
 
0.2%
26.5 1
 
0.1%
ValueCountFrequency (%)
34.4 1
 
0.1%
33.9 1
 
0.1%
33.6 1
 
0.1%
32.8 1
 
0.1%
32.6 1
 
0.1%
32.5 1
 
0.1%
32.2 1
 
0.1%
32.1 1
 
0.1%
31.9 1
 
0.1%
31.8 5
0.3%
Distinct133
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:15.325824image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Length

Max length6
Median length1
Mean length1.3024507
Min length1

Characters and Unicode

Total characters2179
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129 ?
Unique (%)7.7%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
800,4 2
 
1.5%
99,46 2
 
1.5%
658,7 2
 
1.5%
285,3 1
 
0.7%
487,6 1
 
0.7%
456,1 1
 
0.7%
961,7 1
 
0.7%
291,5 1
 
0.7%
511 1
 
0.7%
509,6 1
 
0.7%
Other values (122) 122
90.4%
2026-02-23T20:56:15.695713image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1538
70.6%
, 111
 
5.1%
1 82
 
3.8%
4 68
 
3.1%
6 61
 
2.8%
2 59
 
2.7%
5 54
 
2.5%
8 48
 
2.2%
3 42
 
1.9%
9 41
 
1.9%
Other values (2) 75
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2179
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1538
70.6%
, 111
 
5.1%
1 82
 
3.8%
4 68
 
3.1%
6 61
 
2.8%
2 59
 
2.7%
5 54
 
2.5%
8 48
 
2.2%
3 42
 
1.9%
9 41
 
1.9%
Other values (2) 75
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2179
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1538
70.6%
, 111
 
5.1%
1 82
 
3.8%
4 68
 
3.1%
6 61
 
2.8%
2 59
 
2.7%
5 54
 
2.5%
8 48
 
2.2%
3 42
 
1.9%
9 41
 
1.9%
Other values (2) 75
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2179
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1538
70.6%
, 111
 
5.1%
1 82
 
3.8%
4 68
 
3.1%
6 61
 
2.8%
2 59
 
2.7%
5 54
 
2.5%
8 48
 
2.2%
3 42
 
1.9%
9 41
 
1.9%
Other values (2) 75
 
3.4%
Distinct126
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:15.982561image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.315003
Min length1

Characters and Unicode

Total characters2200
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique115 ?
Unique (%)6.9%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
1,079 2
 
1.5%
0,324 2
 
1.5%
0,898 2
 
1.5%
0,112 2
 
1.5%
0,739 2
 
1.5%
0,203 2
 
1.5%
0,535 2
 
1.5%
0,19 2
 
1.5%
0,321 2
 
1.5%
0,57 2
 
1.5%
Other values (115) 115
85.2%
2026-02-23T20:56:16.359687image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1538
69.9%
, 135
 
6.1%
0 135
 
6.1%
1 64
 
2.9%
2 59
 
2.7%
7 49
 
2.2%
3 48
 
2.2%
5 44
 
2.0%
4 39
 
1.8%
9 32
 
1.5%
Other values (2) 57
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1538
69.9%
, 135
 
6.1%
0 135
 
6.1%
1 64
 
2.9%
2 59
 
2.7%
7 49
 
2.2%
3 48
 
2.2%
5 44
 
2.0%
4 39
 
1.8%
9 32
 
1.5%
Other values (2) 57
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1538
69.9%
, 135
 
6.1%
0 135
 
6.1%
1 64
 
2.9%
2 59
 
2.7%
7 49
 
2.2%
3 48
 
2.2%
5 44
 
2.0%
4 39
 
1.8%
9 32
 
1.5%
Other values (2) 57
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1538
69.9%
, 135
 
6.1%
0 135
 
6.1%
1 64
 
2.9%
2 59
 
2.7%
7 49
 
2.2%
3 48
 
2.2%
5 44
 
2.0%
4 39
 
1.8%
9 32
 
1.5%
Other values (2) 57
 
2.6%

sigsonia_1
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
1673 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1673
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
1673
100.0%

Length

2026-02-23T20:56:16.440008image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-23T20:56:16.484556image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1673
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1673
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1673
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1673
100.0%

F30
Categorical

High correlation 

Distinct16
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
Diego Alejandro Ruiz Fontecha
603 
Jairo Veloza
332 
Julio Becerra
126 
Johanna Gutiérrez
108 
Efrén Gómez
106 
Other values (11)
398 

Length

Max length41
Median length30
Mean length20.375971
Min length1

Characters and Unicode

Total characters34089
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
Diego Alejandro Ruiz Fontecha 603
36.0%
Jairo Veloza 332
19.8%
Julio Becerra 126
 
7.5%
Johanna Gutiérrez 108
 
6.5%
Efrén Gómez 106
 
6.3%
Germán Camargo / Efrén Gómez 86
 
5.1%
Germán Camargo 56
 
3.3%
Jairo Veloza - Diego Ospina -, Diego Ruiz 56
 
3.3%
Joel Rivas 53
 
3.2%
48
 
2.9%
Other values (6) 99
 
5.9%

Length

2026-02-23T20:56:16.560439image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
diego 758
14.7%
ruiz 702
13.6%
alejandro 603
11.7%
fontecha 603
11.7%
jairo 388
7.5%
veloza 388
7.5%
231
 
4.5%
gómez 194
 
3.8%
efrén 194
 
3.8%
camargo 147
 
2.9%
Other values (12) 947
18.4%

Most occurring characters

ValueCountFrequency (%)
3578
 
10.5%
o 3260
 
9.6%
e 3145
 
9.2%
a 2898
 
8.5%
i 2228
 
6.5%
r 2113
 
6.2%
n 1983
 
5.8%
z 1427
 
4.2%
l 1172
 
3.4%
u 971
 
2.8%
Other values (28) 11314
33.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34089
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3578
 
10.5%
o 3260
 
9.6%
e 3145
 
9.2%
a 2898
 
8.5%
i 2228
 
6.5%
r 2113
 
6.2%
n 1983
 
5.8%
z 1427
 
4.2%
l 1172
 
3.4%
u 971
 
2.8%
Other values (28) 11314
33.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34089
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3578
 
10.5%
o 3260
 
9.6%
e 3145
 
9.2%
a 2898
 
8.5%
i 2228
 
6.5%
r 2113
 
6.2%
n 1983
 
5.8%
z 1427
 
4.2%
l 1172
 
3.4%
u 971
 
2.8%
Other values (28) 11314
33.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34089
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3578
 
10.5%
o 3260
 
9.6%
e 3145
 
9.2%
a 2898
 
8.5%
i 2228
 
6.5%
r 2113
 
6.2%
n 1983
 
5.8%
z 1427
 
4.2%
l 1172
 
3.4%
u 971
 
2.8%
Other values (28) 11314
33.2%

CE
Real number (ℝ)

High correlation 

Distinct1494
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2998.9801
Minimum58.31
Maximum59830
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:16.664395image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum58.31
5-th percentile464.36
Q1921.1
median1616
Q33256
95-th percentile9665
Maximum59830
Range59771.69
Interquartile range (IQR)2334.9

Descriptive statistics

Standard deviation4490.4965
Coefficient of variation (CV)1.4973412
Kurtosis44.536219
Mean2998.9801
Median Absolute Deviation (MAD)877.7
Skewness5.5199764
Sum5017293.6
Variance20164559
MonotonicityNot monotonic
2026-02-23T20:56:16.791299image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1006 5
 
0.3%
1101 4
 
0.2%
1911 3
 
0.2%
1307 3
 
0.2%
1864 3
 
0.2%
1115 3
 
0.2%
1447 3
 
0.2%
1240 3
 
0.2%
1218 3
 
0.2%
1137 3
 
0.2%
Other values (1484) 1640
98.0%
ValueCountFrequency (%)
58.31 1
0.1%
68.43 1
0.1%
104.5 1
0.1%
114.1 1
0.1%
126.5 1
0.1%
131.4 1
0.1%
155.7 1
0.1%
172.5 1
0.1%
173.7 1
0.1%
177.4 1
0.1%
ValueCountFrequency (%)
59830 1
0.1%
51260 1
0.1%
47700 1
0.1%
41490 1
0.1%
38850 1
0.1%
36830 1
0.1%
36050 1
0.1%
35440 1
0.1%
33330 1
0.1%
33030 1
0.1%

SAL
Real number (ℝ)

High correlation 

Distinct1259
Distinct (%)75.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7216967
Minimum0.063
Maximum59.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2026-02-23T20:56:16.914694image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Quantile statistics

Minimum0.063
5-th percentile0.2686
Q10.504
median0.88
Q31.782
95-th percentile5.5564
Maximum59.8
Range59.737
Interquartile range (IQR)1.278

Descriptive statistics

Standard deviation3.080298
Coefficient of variation (CV)1.7891061
Kurtosis109.1349
Mean1.7216967
Median Absolute Deviation (MAD)0.469
Skewness8.3215863
Sum2880.3985
Variance9.4882358
MonotonicityNot monotonic
2026-02-23T20:56:17.182150image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.357 6
 
0.4%
0.535 5
 
0.3%
0.702 5
 
0.3%
0.49 5
 
0.3%
0.452 5
 
0.3%
0.398 5
 
0.3%
0.603 5
 
0.3%
0.797 4
 
0.2%
0.359 4
 
0.2%
0.457 4
 
0.2%
Other values (1249) 1625
97.1%
ValueCountFrequency (%)
0.063 1
0.1%
0.07 1
0.1%
0.085 1
0.1%
0.086 1
0.1%
0.089 1
0.1%
0.099 1
0.1%
0.104 1
0.1%
0.112 2
0.1%
0.117 2
0.1%
0.121 1
0.1%
ValueCountFrequency (%)
59.8 1
0.1%
40.36 1
0.1%
31.13 1
0.1%
28.193 1
0.1%
26.76 1
0.1%
24.85 1
0.1%
23.47 1
0.1%
22.91 1
0.1%
22.51 1
0.1%
21 1
0.1%

Interactions

2026-02-23T20:56:06.346570image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:41.651219image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:43.203441image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:44.805742image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:46.302402image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:47.832420image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:49.352274image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:50.902183image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:52.386360image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:54.088204image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:55.492598image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:57.110837image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:58.595574image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:00.307223image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:01.724206image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:03.265348image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:04.752286image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:06.436460image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:41.758483image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:43.291863image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:44.896565image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:46.391251image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:47.927545image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:49.437535image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:50.982273image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:52.474684image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:54.174501image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:55.584214image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:57.204158image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:58.688267image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:00.400841image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:01.812480image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:03.352726image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:04.834249image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:06.521995image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:41.852139image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:43.380965image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:44.991980image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:46.473979image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:48.059891image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:49.530138image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:51.074694image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:52.572316image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:54.262863image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:55.664618image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:57.296111image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:58.782286image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:00.482541image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:01.896518image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:03.433750image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:04.924593image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:06.605470image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:41.966423image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:43.467906image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:45.078594image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:46.558052image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:48.157546image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:49.632432image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:51.154579image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:52.672321image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:54.342510image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:55.763271image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:57.380449image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:58.877007image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:00.572289image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:01.986372image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:03.524613image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:05.008299image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:06.689362image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:42.054631image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:43.558118image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:45.166190image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:46.637362image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:48.250527image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:49.715724image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:51.234699image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:52.762502image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:54.424720image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:55.852427image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:57.464609image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:58.962649image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:00.653728image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:02.066803image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:03.614479image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:05.087634image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:06.771713image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:42.146222image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:43.640223image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:45.255147image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:46.717741image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:48.345184image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:49.796905image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:51.324669image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:52.867216image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:54.508320image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:55.951020image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:57.542575image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:59.052390image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:00.746821image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:02.145263image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:03.713463image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:05.174551image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:06.853999image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:42.223747image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:43.724366image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:45.334323image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:46.798802image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:48.423297image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:49.870714image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:51.404967image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:52.954383image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:54.584433image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:56.024440image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:57.632411image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:59.142869image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:00.828805image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:02.228241image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:03.800801image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:05.250902image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:06.934917image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:42.322111image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:43.808759image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:45.422309image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:46.998370image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:48.506079image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:49.945313image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:51.488212image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:53.041322image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:54.662626image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:56.114693image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:57.712298image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:59.234494image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:00.906485image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:02.306201image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:03.889659image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:05.335035image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:07.020207image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:42.405981image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:43.895970image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:45.508473image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:47.081769image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:48.591895image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:50.159646image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:51.574413image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:53.124499image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:54.742612image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:56.202105image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:57.802407image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:59.338857image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:00.983626image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:02.385720image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:03.981756image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:05.424481image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:07.096739image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:42.494376image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:43.989220image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:45.588417image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:47.158662image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:48.666905image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:50.232193image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:51.654804image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:53.364733image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:54.818354image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:56.288310image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:57.897956image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:59.432288image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:01.064892image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:02.469997image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:04.068421image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:05.504284image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:07.179560image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:42.590482image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:44.081012image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:45.689960image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:47.247190image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:48.754552image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:50.315092image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:51.742219image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:53.449585image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:54.906296image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:56.372342image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:57.986321image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:59.526244image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:01.144939image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:02.552814image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:04.162109image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:05.589954image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:07.260186image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:42.679941image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:44.288433image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:45.779025image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:47.327489image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:48.836098image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:50.397766image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:51.822292image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:53.539187image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:54.995801image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:56.594559image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:58.072646image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:59.764730image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:01.224514image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:02.634403image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:04.239374image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:05.686550image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:07.343795image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:42.771453image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:44.376797image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:45.869161image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:47.416491image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:48.928042image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:50.477371image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:51.920558image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:53.633592image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:55.074777image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:56.682535image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:58.165560image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:59.856733image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:01.306471image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:02.719886image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:04.334507image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:05.918081image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:07.425450image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:42.851839image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:44.460925image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:45.948861image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:47.493087image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:49.009775image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:50.555143image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:51.999242image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:53.718272image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:55.164290image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:56.762641image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:58.242455image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:59.942461image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:01.374908image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:02.928807image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:04.414649image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:05.999960image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:07.514203image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:42.935554image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:44.538054image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:46.039423image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:47.575570image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:49.098734image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:50.643748image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:52.083418image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:53.812424image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:55.242519image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:56.854905image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:58.336310image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:00.024280image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:01.455609image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:03.006510image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:04.498174image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:06.075489image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:07.594258image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:43.024428image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:44.627348image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:46.120705image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:47.664644image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:49.182664image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:50.732069image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:52.215893image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:53.907165image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:55.331019image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:56.942483image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:58.422385image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:00.127382image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:01.564189image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:03.094313image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:04.576255image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:06.167109image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:07.684403image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:43.115763image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:44.714686image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:46.210612image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:47.745636image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:49.267509image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:50.817604image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:52.302432image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:53.995021image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:55.416568image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:57.026079image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:55:58.506626image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:00.219751image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:01.634441image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:03.174342image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:04.664217image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
2026-02-23T20:56:06.255701image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/

Correlations

2026-02-23T20:56:17.298914image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
CECond_SecoCond_seco1CondiciónF30ID_PROYECTID_TOTALMétodo_deNo_ConsecuOBJECTIDObservacioSALSTD_secoSal_secoT_SecoT_seco1Tipo_de_CaTipo_de_NiUso_del_SuXYpH_HumedopH_secosigsonia__
CE1.0000.948-0.1660.2570.096-0.159-0.2380.000-0.124-0.2380.0000.9870.9300.9380.362-0.1830.1120.0000.2620.5010.604-0.1790.169-0.259
Cond_Seco0.9481.000-0.3730.2570.096-0.175-0.2720.000-0.096-0.2720.0000.9350.9830.9910.448-0.3840.1110.0000.2620.5510.657-0.3810.267-0.471
Cond_seco1-0.166-0.3731.0000.0710.1880.0460.0890.000-0.0910.0890.000-0.158-0.373-0.372-0.4220.9970.1350.0640.000-0.215-0.2510.997-0.4200.867
Condición0.2570.2570.0711.0000.2040.0560.1400.0710.0000.1310.0000.2910.2560.2910.0000.1620.1400.1130.2740.1930.1630.1420.0000.000
F300.0960.0960.1880.2041.0000.1550.5870.3580.1370.5690.3560.0920.1030.0940.2670.3520.3720.3550.4640.4390.4620.3180.2940.320
ID_PROYECT-0.159-0.1750.0460.0560.1551.000-0.0850.1050.291-0.0850.058-0.154-0.175-0.171-0.0220.0510.1400.0770.152-0.179-0.2200.050-0.0480.098
ID_TOTAL-0.238-0.2720.0890.1400.587-0.0851.0000.3620.1121.0000.134-0.241-0.271-0.277-0.4670.1000.2730.3240.411-0.187-0.3370.098-0.2730.234
Método_de0.0000.0000.0000.0710.3580.1050.3621.0000.0510.3640.1510.0000.0000.0000.1480.1240.3640.8710.3440.2510.2360.1530.1960.000
No_Consecu-0.124-0.096-0.0910.0000.1370.2910.1120.0511.0000.1120.100-0.121-0.093-0.0950.062-0.0870.0590.0600.072-0.096-0.102-0.0880.049-0.046
OBJECTID-0.238-0.2720.0890.1310.569-0.0851.0000.3640.1121.0000.136-0.241-0.271-0.277-0.4670.1000.2690.3250.417-0.187-0.3370.098-0.2730.234
Observacio0.0000.0000.0000.0000.3560.0580.1340.1510.1000.1361.0000.0000.0000.0000.0780.0710.1840.2760.1690.0700.1130.0000.0000.567
SAL0.9870.935-0.1580.2910.092-0.154-0.2410.000-0.121-0.2410.0001.0000.9300.9440.360-0.1750.1010.0000.2780.5100.607-0.1700.160-0.250
STD_seco0.9300.983-0.3730.2560.103-0.175-0.2710.000-0.093-0.2710.0000.9301.0000.9870.441-0.3840.1140.0000.2500.5510.657-0.3810.263-0.471
Sal_seco0.9380.991-0.3720.2910.094-0.171-0.2770.000-0.095-0.2770.0000.9440.9871.0000.449-0.3840.1020.0000.2780.5620.663-0.3800.263-0.471
T_Seco0.3620.448-0.4220.0000.267-0.022-0.4670.1480.062-0.4670.0780.3600.4410.4491.000-0.4280.2270.1690.2210.3180.483-0.4280.359-0.463
T_seco1-0.183-0.3840.9970.1620.3520.0510.1000.124-0.0870.1000.071-0.175-0.384-0.384-0.4281.0000.1840.1630.224-0.229-0.2640.996-0.4270.885
Tipo_de_Ca0.1120.1110.1350.1400.3720.1400.2730.3640.0590.2690.1840.1010.1140.1020.2270.1841.0000.3960.3200.2550.3130.1120.1700.229
Tipo_de_Ni0.0000.0000.0640.1130.3550.0770.3240.8710.0600.3250.2760.0000.0000.0000.1690.1630.3961.0000.4080.2450.2110.1850.1910.000
Uso_del_Su0.2620.2620.0000.2740.4640.1520.4110.3440.0720.4170.1690.2780.2500.2780.2210.2240.3200.4081.0000.3440.3690.2680.3700.000
X0.5010.551-0.2150.1930.439-0.179-0.1870.251-0.096-0.1870.0700.5100.5510.5620.318-0.2290.2550.2450.3441.0000.724-0.2250.159-0.365
Y0.6040.657-0.2510.1630.462-0.220-0.3370.236-0.102-0.3370.1130.6070.6570.6630.483-0.2640.3130.2110.3690.7241.000-0.2600.405-0.394
pH_Humedo-0.179-0.3810.9970.1420.3180.0500.0980.153-0.0880.0980.000-0.170-0.381-0.380-0.4280.9960.1120.1850.268-0.225-0.2601.000-0.4230.880
pH_seco0.1690.267-0.4200.0000.294-0.048-0.2730.1960.049-0.2730.0000.1600.2630.2630.359-0.4270.1700.1910.3700.1590.405-0.4231.000-0.470
sigsonia__-0.259-0.4710.8670.0000.3200.0980.2340.000-0.0460.2340.567-0.250-0.471-0.471-0.4630.8850.2290.0000.000-0.365-0.3940.880-0.4701.000

Missing values

2026-02-23T20:56:07.864460image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-23T20:56:08.094626image/svg+xmlMatplotlib v3.10.8, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

OBJECTIDID_TOTALID_PROYECTPlanchaNo_ConsecuTipo_de_CaSitioOrigenXYUso_del_SuCondiciónProf_PozoTipo_de_NiMétodo_deProf_SecoProf_HúmeObservaciopH_secoCond_SecoT_SecoSTD_secoSal_secosigsonia__pH_HumedoCond_seco1T_seco1STD_Seco1Sal_seco_1sigsonia_1F30CESAL
01111PozoSiapanaCentral1299675.9701827467.8007.503060.031.21500.7356551.683000.00.000.00.03060.01.6830
12222AljibeIruapaaCentral1299085.7371828796.8787.121585.031.4777.3418340.871750.07.141620.030.31585.00.8718
23333AljibeKashinasCentral1298371.6751829001.2927.07995.032.7487.9843060.547250.07.39917.028.8995.00.5473
34444AljibePolujaliiCentral1298003.6531829238.5337.371339.033.3656.6944580.736450.07.421394.030.11339.00.7365
45555PozoSiapanaCentral1299536.0701826712.3937.691553.00.0761.6478670.854150.07.641611.034.41553.00.8541
56661ManantialWitpaCentral1289335.0541822647.4607.293110.030.11525.2574791.710500.07.473100.030.73110.01.7105
67771AljibeYoil-ChorroCentral1283846.2151805206.6246.752417.031.61185.3849931.329350.00.000.00.02417.01.3294
78882AljibeCementerio Flor de La GuajiraCentral1292863.7001800658.6617.928690.031.64261.8930854.779500.00.000.00.08690.04.7795
89991AljibeEpeyiliCentral1293911.4011833578.5346.851205.030.6590.9759690.662750.07.151350.030.61205.00.6627
91111113ManantialUchimaCentral1293490.7601834412.8388.091429.029.7700.8337420.785950.07.991878.028.01429.00.7860
OBJECTIDID_TOTALID_PROYECTPlanchaNo_ConsecuTipo_de_CaSitioOrigenXYUso_del_SuCondiciónProf_PozoTipo_de_NiMétodo_deProf_SecoProf_HúmeObservaciopH_secoCond_SecoT_SecoSTD_secoSal_secosigsonia__pH_HumedoCond_seco1T_seco1STD_Seco1Sal_seco_1sigsonia_1F30CESAL
166316741800128ManantialFinca Las MaríasCentral1119023.01657606.0ForestalProductivo0.00.00.00.00.02959.07.29337.926.7166,10,213Diego Ruiz337.90.213
166416751801229ManantialCentral1117405.01657727.0ForestalProductivo0.00.00.00.00.02084.06.91479.826.7235,60,282Diego Ruiz479.80.282
166516761802330PozoCentral1117152.01657560.0GanaderiaReserva250EstáticoSonda Eléctrica11,830.00.00.00.00.0952.67.121050.028.6514,40,57Diego Ruiz1050.00.570
166616771803431ManantialFca Ls CombasCentral1117068.01650340.0ForestalProductivo0.00.00.00.00.0835.57.35196.023.1586,70,643Diego Ruiz196.00.643
16671678180452ManantialCentral1114278.01647612.0ForestalProductivo0.00.00.00.00.02579.07.26387.725.7190,50,237Diego Ruiz387.70.237
16681679180563AljibeMaximinaCentral1112794.01649442.0ForestalReservaEstáticoSonda Eléctrica4,08Nivel de referencia, a topo de los anillos0.00.00.00.00.0640.87.151561.026.0768,20,835Diego Ruiz1561.00.835
166916801806732ManantialArroyo HondoCentral1118157.01656141.0ForestalProductivo0.00.00.00.00.03445.07.38290.326.9142,80,19Diego Ruiz290.30.190
167016811807810ManantialEl ManantialCentral1127604.01669373.0ForestalProductivo0.00.00.00.00.01960.06.99510.227.0250,50,297Diego Ruiz510.20.297
167116821808911PozoFca La fortunaCentral1125437.01669834.0Agricultura85EstáticoSonda Eléctrica18,03No posee tubería de Niveles0.00.00.00.00.01524.06.91656.232.5322,10,371Diego Ruiz656.20.371
1672168318091118PozoComunidad ZahinoCentral1138629.01713505.0ForestalProductivo84Pozo encendido a las 8 a.m. Se bajó la sonda hasta los 60 m de prof y no se encontró nivel0.00.00.00.00.01361.06.99734.630.2360,50,409Diego Ruiz734.60.409